1981-2012年辽宁省春播期土壤相对湿度月尺度数据重建

辽宁省现有测站春播期土壤相对湿度数据存在不连续性及长时间序列缺失问题。以海城站为例,分析现有土壤相对湿度（0-20 cm）与气象因子及临近站点土壤相对湿度的相关关系,构建海城春播期土壤相对湿度统计回归模型,模拟缺测时段春播期土壤相对湿度。进而以此方法重建辽宁省20个观测站1981-2012年春播期土壤相对湿度月尺度数据。结果表明：海城土壤相对湿度与降水量和秋季封冻雨关联较大,相关系数分别超过0.60和0.30,与同期临近站点本溪站土壤相对湿度相关性也超过0.40,依据该3要素构建的4月和5月土壤相湿度统计回归模型复相关系数R2分别达0.79和0.77,模拟结果与实测资料平均相对误差为2.6 %,模拟效果较好;对辽宁省其他数据缺失站点构建的回归模型复相关系数均高于0.50,模型拟合精度优于85 %,拟合值和实测值平均相对误差基本控制在15 %以内,较好完成辽宁省20个测站1981-2012年春播期土壤相对湿度月尺度数据重建。     

大理机场雷暴特征及潜势预报分析

利用大理机场5年天气观测资料和FNL 1.0X1.0数据,对大理机场雷暴特征及潜势预报进行分析,结果发现：大理机场全年各月都有可能出现雷暴天气,雷暴天气主要出现在6~9月,每年7月和8月雷暴天气出现最为频繁;雷暴天气持续时间0~1小时的次数最多,持续时间1~2小时的雷暴也比较常见,持续时间4~6小时的次数较少,没有出现持续时间6小时以上的雷暴;雷暴可以出现在大理机场的任何方向,出现在东边的次数最多,出现在天顶的次数最少;雷暴初期平均在1月31日,雷暴终期平均在11月14日,雷暴的初期和终期年际差别较大。选取对流有效位能、500hPa相对湿度、0度层高度、近地表四层等压面的抬升指数和可降水量作为预报因子建立大理机场雷暴预报方程,预报方程是显著的,有较好的雷暴潜势预报能力。     

Detection of impervious surface change with multitemporal Landsat images in an urban–rural frontier

Mapping and monitoring impervious surface dynamic change in a complex urban-rural frontier with medium or coarse spatial resolution images is a challenge due to the mixed pixel problem and the spectral confusion between impervious surfaces and other non-vegetation land covers. This research selected Lucas do Rio Verde County in Mato Grosso State, Brazil as a case study to improve impervious surface estimation performance by the integrated use of Landsat and QuickBird images and to monitor impervious surface change by analyzing the normalized multitemporal Landsat-derived fractional impervious surfaces. This research demonstrates the importance of two-step calibrations. The first step is to calibrate the Landsat-derived fraction impervious surface values through the established regression model based on the QuickBird-derived impervious surface image in 2008. The second step is to conduct the normalization between the calibrated 2008 impervious surface image with other dates of impervious surface images. This research indicates that the per-pixel based method overestimates the impervious surface area in the urban-rural frontier by 50%-60%. In order to accurately estimate impervious surface area, it is necessary to map the fractional impervious surface image and further calibrate the estimates with high spatial resolution images. Also normalization of the multitemporal fractional impervious surface images is needed to reduce the impacts from different environmental conditions, in order to effectively detect the impervious surface dynamic change in a complex urban-rural frontier. The procedure developed in this paper for mapping and monitoring impervious surface area is especially valuable in urban-rural frontiers where multitemporal Landsat images are difficult to be used for accurately extracting impervious surface features based on traditional per-pixel based classification methods as they cannot effectively handle the mixed pixel problem.     

An improved method for evaluating the seasonal variability of total suspended sediment flux field in the Yellow and East China Seas

The suspended sediment flux field in the Yellow and East China Seas(YECS) displays its seasonal variability.A new method is introduced in this paper to obtain the flux field via retrieval of ocean color remote sensing data,statistical analysis of historical suspended sediment concentration data,and numerical simulation of three-dimensional(3D) flow velocity.The components of the sediment flux field include(i) surface suspended sediment concentration inverted from ocean color remote sensing data;(ii) vertical distribution of suspended sediment concentration obtained by statistical analysis of historical observation data;and(iii) 3D flow field modeled by a numerical simulation.With the improved method,the 3D suspended sediment flux field in the YECS has been illustrated.By comparison with the suspended sediment flux field solely based on the numerical simulation of a suspended sediment transport model,the suspended sediment flux field obtained by the improved method is found to be more reliable.The 3D suspended sediment flux field from ocean colour remote sensing and in situ observation are more closer to the reality.Furthermore,by quantitatively analyzing the newly obtained suspended sediment flux field,the quantity of sediment erosion and deposition within the different regions can be evaluated.The sediment exchange between the Yellow Sea and the East China Sea can be evident.The mechanism of suspended sediment transport in the YECS can be better understood.In particular,it is suggested that the long-term transport of suspended sediment is controlled mainly by the circulation pattern,especially the current in winter.     

Determinants of public goods investment in rural communities in mountainous areas of Sichuan Province,China

This study aims to investigate two important issues： what are the determinants of public goods investment and what is the government＇s investment behavior in mountainous areas. The impacts of natural conditions, target, and demand elements on public goods investment are analyzed with statistical method, and the determinants of public goods investment in the areas are obtained by using population-weighted and stepwise regression models with Eviews6.0 software with survey data in 2008 and calculated data based on GIS of 20 typical villages in mountainous regions in Sichuan, China. The results showed： （1） natural conditions are the important determinants of public investment. Mountainous villages with steep slope have relatively high levels of investment; （2） concentration of population and the educational levels of the village leaders also have important impacts on public goods investment; （3） the government is more concerned with public investment resources particularly in areas characterized by fragile ecological environment and poor agricultural output. These results suggest that the current investment strategy helps to reduce disparities in regional development.     

An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD

Characterizing and quantifying distributions of shrubland ecosystem components is one of the major challenges for monitoring shrubland vegetation cover change across the United States. A new approach has been developed to quantify shrubland components as fractional products within National Land Cover Database (NLCD). This approach uses remote sensing data and regression tree models to estimate the fractional cover of shrubland ecosystem components. The approach consists of three major steps: field data collection, high resolution estimates of shrubland ecosystem components using WorldView-2 imagery, and coarse resolution estimates of these components across larger areas using Landsat imagery. This research seeks to explore this method to quantify shrubland ecosystem components as continuous fields in regions that contain wide-ranging shrubland ecosystems. Fractional cover of four shrubland ecosystem components, including bare ground, herbaceous, litter, and shrub, as well as shrub heights, were delineated in three ecological regions in Arizona, Florida, and Texas. Results show that estimates for most components have relatively small normalized root mean square errors and significant correlations with validation data in both Arizona and Texas. The distribution patterns of shrub height also show relatively high accuracies in these two areas. The fractional cover estimates of shrubland components, except for litter, are not well represented in the Florida site. The research results suggest that this method provides good potential to effectively characterize shrubland ecosystem conditions over perennial shrubland although it is less effective in transitional shrubland. The fractional cover of shrub components as continuous elements could offer valuable information to quantify biomass and help improve thematic land cover classification in arid and semiarid areas.     

Assessment of regression kriging for spatial interpolation – comparisons of seven GIS interpolation methods

As an important GIS function, spatial interpolation is one of the most often used geographic techniques for spatial query, spatial data visualization, and spatial decision-making processes in GIS and environmental science. However, less attention has been paid on the comparisons of available spatial interpolation methods, although a number of GIS models including inverse distance weighting, spline, radial basis functions, and the typical geostatistical models (i.e. ordinary kriging, universal kriging, and cokriging) are already incorporated in GIS software packages. In this research, the conceptual and methodological aspects of regression kriging and GIS built-in interpolation models and their interpolation performance are compared and evaluated. Regression kriging is the combination of multivariate regression and kriging. It takes into consideration the spatial autocorrelation of the variable of interest, the correlation between the variable of interest and auxiliary variables (e.g., remotely sensed images are often relatively easy to obtain as auxiliary variables), and the unbiased spatial estimation with minimized variance. To assess the efficiency of regression kriging and the difference between stochastic and deterministic interpolation methods, three case studies with strong, medium, and weak correlation between the response and auxiliary variables are compared to assess interpolation performances. Results indicate that regression kriging has the potential to significantly improve spatial prediction accuracy even when using a weakly correlated auxiliary variable.     

Predicting aboveground biomass of woody encroacher species in semi-arid rangelands,Ethiopia

Species-specific allometric models were developed to predict aboveground biomass (AGB) of eight woody species in the Borana rangelands, Ethiopia. The 23 equations developed (8 species; three biomass components: total aboveground, stem and branches) fit the data well to predict total AGB and by components for each of the species (r² > 0.70; p < 0.001). The AGB of tree shaped species (e.g., Acacia bussei and Acacia etabaica) were significantly predicted from a single predictor (circumference of the stem at ankle height), with a high coefficient of determination (r² > 0.95; p < 0.001). In contrast, the AGB of bushy shrubs (e.g., Acacia oerfota) was more effectively predicted by using the canopy volume (r² = 0.84; p < 0.001). Shrubs with a tall stem and an umbrella-like canopy structure (e.g., Acacia mellifera) were most accurately predicted by a combination of both circumference of the stem at ankle height and canopy volume (r² = 0.95; p < 0.001). Hence, our species-specific allometric models could accurately estimate their woody aboveground biomass in a semi-arid savanna ecosystem of southern Ethiopia. These equations will help in future carbon-trade discussions in times of climate change and CO₂ emission concerns and mitigation strategies.     

Assessing Mongolian snow disaster risk using livestock and satellite data

In Mongolia, several record-breaking disastrous dzuds (mass livestock loss directly induced by harsh winter conditions but often influenced by drought in the previous summer) occurred from 1999 to 2003. To understand the mechanism of this climatic disaster, we conducted a tree regression analysis. The predictor variables included two indices developed from remote sensing data—the Normalized Difference Vegetation Index (NDVI) and the Snow Water Equivalent (SWE)—as well as the previous year's livestock numbers and mortality rates. According to the model, serious livestock mortality was associated with low NDVI values (i.e., poor vegetation) in August of the previous year, high SWE values (i.e., significant snow accumulation) in December of the previous year, a high previous year's mortality, and high previous year's livestock population. This result suggests that for dzud risk assessment, we need to monitor snowfall in winter, the vegetation condition in the previous summer, and the density and health condition of the livestock. The tree-based model developed in this study is effective only for a white dzud (deep snow), the most common type of dzud. The large cross-validation error indicates that more data are needed before using the model in order to make predictions.